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Found 5 papers, 4 papers with code
Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network
Notably, with STR and cutoff, SNN achieves 2. 14 to 2. 89 faster in inference compared to the pre-configured timestep with near-zero accuracy drop of 0. 50% to 0. 64% over the event-based datasets.
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains.
Randomized Adversarial Training via Taylor Expansion
The randomized weights enable our design of a novel adversarial training method via Taylor expansion of a small Gaussian noise, and we show that the new adversarial training method can flatten loss landscape and find flat minima.
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
The Top-K cutoff technique optimises the inference of SNN, and the regularisation are proposed to affect the training and construct SNN with optimised performance for cutoff.
A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network from Convolutional Neural Network
In this paper, we argue that this trend of "energy for accuracy" is not necessary -- a little energy can go a long way to achieve the near-zero accuracy loss.
